The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm
Open accessAbstract
NLP tasks are often limited by scarcity of manually annotated data.In social media sentiment analysis and related tasks, researchers have therefore used binarized emoticons and specific hashtags as forms of distant supervision.Our paper shows that by extending the distant supervision to a more diverse set of noisy labels, the models can learn richer representations.Through emoji prediction on a dataset of 1246 million tweets containing one of 64 common emojis we obtain state-of-theart performance on 8 benchmark datasets within emotion, sentiment and sarcasm detection using a single pretrained model.Our analyses confirm that the diversity of our emotional labels yield a performance improvement over previous distant supervision approaches.
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Cited by
Transformer models for text-based emotion detection: a review of BERT-based approaches
Acheampong, F. A., Nunoo-Mensah, H., & Chen, W. (2021) · Artificial Intelligence Review · doi:10.1007/s10462-021-09958-2 · Cited by 659
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
Srivastava, A., Rastogi, A., Rao, A., Shoeb, A. A. M., Abid, A., Fisch, A., Brown, A. R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al. (2022) · arXiv · doi:10.48550/arxiv.2206.04615 · Cited by 569
A Systematic Review of Emoji: Current Research and Future Perspectives
Bai, Q., Dan, Q., Mu, Z., & Yang, M. (2019) · Frontiers in Psychology · doi:10.3389/fpsyg.2019.02221 · Cited by 502
Text‐based emotion detection: Advances, challenges, and opportunities
Acheampong, F. A., Wenyu, C., & Nunoo‐Mensah, H. (2020) · Engineering Reports · doi:10.1002/eng2.12189 · Cited by 367
Sesagiri Raamkumar, A., Tan, S. G., & Wee, H. L. (2020) · Journal of Medical Internet Research · doi:10.2196/19334 · Cited by 166
Metadata
- Authors
- Felbo, B., Mislove, A., Søgaard, A., Rahwan, I., & Lehmann, S.
- Venue
- Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
- Published
- 2017
- Pages
- 1615-1625
- OpenAlex
- W2740582239
- Cited by
- 431
- References
- 5
- FWCI
- 17.66
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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- Felbo, B., Mislove, A., Søgaard, A., Rahwan, I., & Lehmann, S. (2017). Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm. Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. https://doi.org/10.18653/v1/d17-1169
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